A Multi-perspective Study Of The Neural Representation Of Lexical Semantics

Research on the mental organization of semantic concepts has made remarkable advances by virtue of psychological studies on mental lexicon and the flourishing of artificial intelligence,but the full picture is still unknown.In the field of computer science,a large number of word embedding models derived from text corpus have been created and are widely used in real semantic scenarios,but the underlying mechanisms have not been clearly addressed,which has constrained the applications associated with deep semantic processing.In the field of psychology,free association networks have shown the capability in constructing models that contain multidimensional semantic information and real psychological features by covering large numbers of words(nodes)and associations(edges).However,no semantic association network database in Chinese of comparable scale to the mainstream corpus-derived models has been established.Thus,establishing psychologically valid large-scale semantic network is necessary for the comparison of the above-mentioned models and for advancing the research of semantic concepts.Research in cognitive neuroscience has discovered that semantic processing involves widely distributed brain regions,suggesting distributed encoding of semantics.Recent methodological developments,especially multi-voxel pattern analysis methods,have allowed investigations of semantic features in a multidimensional approach.On the other hand,due to the lack of effective theoretical models,previously investigated semantic conditions have been mostly limited to manually specified categories such as abstract vs.concrete and animate vs.inanimate,where potentially significant neurosemantic features might be ignored.In order to comprehensively and accurately investigate the neural representations of semantics,it is crucial to properly select semantic network models and reduce subjective bias in experimental design.The present study uses word association paradigm to build a large-scale association network of Chinese words,the SWOW-ZH.Neural representations of word concepts are compared with the representations in two types of theoretical models,one being the SWOW-ZH,and the other being the word embedding models,Word2 Vec and Concept Net.To avoid bias in acquiring the target semantic categories,stimulus words are determined in a data-driven approach: community detection algorithm is applied on each of the three theoretical models,resulting in 72two-character words from 9 reliable communities that are common across the models.Representational dissimilarity matrices of the stimuli are constructed at three levels of granularity,namely the community level(the coarsest),the cluster level,and the node level(the most fine-grained).Regions of interests across the whole brain are searched for the representational similarities to different theoretical models at different granularities,based on data measured by functional magnetic resonance imaging during a semantic judgment task.Neural representations of concepts are found to be most consistent with the SWOW-ZH at all levels of concept granularity at the whole-brain level.Specifically,at community level,regions that are most similar to SWOW-ZH include a wide range of temporoparietal and lateral occipital areas.While at node level,significant similarities are mostly localized in the anterior temporal lobe.First,these findings reveal that free association network resembles the neural representation of semantic concepts to a larger than the word embedding models,suggesting a better characterization of mental features of concepts.Second,neural semantic representations are found hierarchically structured along the gradient from temporooccipital cortices to the anterior temporal lobe.Third,the anterior temporal lobe encodes semantic information of different granularities,supporting the Hub-and-spoke Model that proposes the anterior temporal area as the hub of integrating semantic features from multiple sources.Overall,the findings have advanced our understanding of the neural representations of semantic knowledge,and have highlighted the significance of semantic network constructed from large-scale behavioral data to the studies of semantics in various fields.